The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur
Open accessAbstract
Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium–Sulphur (Li–S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reduced-order electrochemical models and equivalent circuit models. The particular challenges associated with Li–S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed.
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Cites
Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell
Doyle, M., Fuller, T. F., & Newman, J. (1993) · Journal of The Electrochemical Society · doi:10.1149/1.2221597 · Cited by 4,328
Accurate electrical battery model capable of predicting runtime and I-V performance
Chen, M., & Rincon-Mora, G. A. (2006) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2006.874229 · Cited by 2,427
A comparative study of equivalent circuit models for Li-ion batteries
Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179
Simulation and Optimization of the Dual Lithium Ion Insertion Cell
Fuller, T. F., Doyle, M., & Newman, J. (1994) · Journal of The Electrochemical Society · doi:10.1149/1.2054684 · Cited by 2,057
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385
Modeling Lithium Ion battery degradation in electric vehicles
Millner, A. (2010) · 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply · doi:10.1109/citres.2010.5619782 · Cited by 397
The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer
Xu, J., Mi, C. C., Cao, B., Deng, J., Chen, Z., & Li, S. (2014) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2013.2287375 · Cited by 367
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A review of Battery Electric Vehicle technology and readiness levels
Mahmoudzadeh Andwari, A., Pesiridis, A., Rajoo, S., Martinez-Botas, R., & Esfahanian, V. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.03.138 · Cited by 784
A review on battery thermal management in electric vehicle application
Xia, G., Cao, L., & Bi, G. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.09.046 · Cited by 641
Tran, M.-K., Mathew, M., Janhunen, S., Panchal, S., Raahemifar, K., Fraser, R., & Fowler, M. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103252 · Cited by 564
Mao, Y., Li, G., Guo, Y., Li, Z., Liang, C., Peng, X., & Lin, Z. (2017) · Nature Communications · doi:10.1038/ncomms14628 · Cited by 518
A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells
Barai, A., Uddin, K., Dubarry, M., Somerville, L., McGordon, A., Jennings, P., & Bloom, I. (2019) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2019.01.001 · Cited by 319
Metadata
- Authors
- Fotouhi, A., Auger, D. J., Propp, K., Longo, S., & Wild, M.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 56
- Pages
- 1008-1021
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2215994231
- Cited by
- 780
- References
- 7
- FWCI
- 57.02
- Type
- journal article
- Language
- en
- Open access
- green
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- Fotouhi, A., Auger, D. J., Propp, K., Longo, S., & Wild, M. (2016). A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2015.12.009
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.